• D. Dhanalakshmi
• G. Rajendran

Community detection is a fundamental task in network analysis, aimed at uncovering meaningful structures and relationships within large-scale graphs. This study presents a comparative analysis of three prominent algorithms — Louvain, Leiden, and Infomap — each representing distinct approaches to community detection. The Louvain algorithm employs hierarchical modularity optimization for efficient clustering but suffers from the resolution limit problem. The Leiden algorithm improves upon Louvain by introducing a refinement phase that ensures well-connected, stable partitions and overcomes modularity-related limitations. Infomap, in contrast, utilizes an information-theoretic approach based on random walks and Minimum Description Length (MDL) to capture the natural flow of information within networks. The performance of these algorithms is evaluated using multiple benchmark datasets, including the Reddit Hyperlink Network, Amazon Co-Purchasing Network, DBLP Collaboration Network, and Twitch Gamers Network. Evaluation metrics such as Modularity (Q), Normalized Mutual Information (NMI), and Execution Time (T) are analyzed to assess accuracy, scalability, and stability. Results indicate that Leiden achieves the most stable partitions, Infomap effectively identifies flow-based communities, and Louvain offers the fastest execution with reasonable modularity. This comprehensive comparison highlights the strengths and limitations of each algorithm and provides insights for selecting suitable methods in large-scale community detection tasks.

Community Detection, Louvain Algorithm, Leiden Algorithm, Infomap, Modularity Optimization, Information Theory, Random Walks, Network Analysis, Normalized Mutual Information (NMI), Complex Networks, Benchmark Datasets

"COMPARATIVE STUDY OF LOUVAIN, LEIDEN, AND INFOMAP ALGORITHMS FOR COMMUNITY DETECTION IN COMPLEX NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.j451-j461, June-2025, Available :http://www.jetir.org/papers/JETIR2506955.pdf

"COMPARATIVE STUDY OF LOUVAIN, LEIDEN, AND INFOMAP ALGORITHMS FOR COMMUNITY DETECTION IN COMPLEX NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppj451-j461, June-2025, Available at : http://www.jetir.org/papers/JETIR2506955.pdf